Plant monitoring methods and uses thereof

Abstract

This invention relates to a method for characterising the pressure regulatory behaviour of a plant (e.g., a plurality of plants, e.g., a plurality of plants comprising different plant species), and in certain embodiments, characterising stomatal-driven pressure regulation in trees.

Description

PLANT MONITORING METHODS AND USES THEREOF RELATED APPLICATIONS

[0001] This application claims priority from Australian Provisional Patent Application No.2024903576 filed on 1 November 2025, the entire content of which is incorporated by reference.TECHNICAL FIELD

[0002] The present disclosure relates generally to a method for characterising the pressure regulatory behaviour of plants, and in particular, stomatal-driven pressure regulation in trees. The present disclosure also extends to identifying a threshold value of soil water potential below which plant activity is limited.BACKGROUND

[0003] Water pressure, or more correctly, apoplastic water potential inside the walls of the plant body, is the tension that connects living cells in the plant to water in the soil. This tension enables plants to pull water passively from the soil, but it can also pose a lethal risk when unregulated by the plant. Decreasing soil moisture levels or high rates of transpiration caused by high vapour pressure deficit (VPD) or a combination of both force plant water potential to become more negative, increasing the water tension in the plant body towards damaging values, referred to here as a state of “hypertension”. Under conditions of hypertension, the water column in the vascular system begins to break down in a process commonly called xylem cavitation, which occurs as air bubbles are pulled into xylem conduits where they expand, blocking the xylem. Xylem cavitation disconnects the plant from the soil and has been causally linked to leaf tissue damage and strongly correlated with plant death.

[0004] Xylem cavitation may be the most obvious damage linked to uncontrolled hypertension in the plant, but other symptoms of excessive xylem tension, such as tissue collapse in leaves and roots also present a clear selection pressure pushing plants to regulate their xylem water potential by the action of stomata. Stomatal pores on the leaf surface enable strong control of transpiration, providing the mechanism for plants to regulate water potential gradients inside the plant, and to restrict soil dehydration. The stringency with which stomata control water potential has been suggested to vary between species, and may be an important axis of variation in water use and survival strategy. Because of its potential implications for plant performance andsurvival during drought, various metrics have emerged that attempt to classify plant species based on the degree of homeostasis in stem water potential ( Pstem) as soil dehydrates (declining soil water potential;soil), such as hydroscape area and isohydricity. However, the current view on categorizing species using existing metrics suggests that the mode of PΨstemregulation is highly unpredictable, with strong variation described across species.

[0005] One pervasive limitation to current methods of characterisation is that plant responses are typically measured as a function of Ψsoil, while omitting other environmental factors such as the atmospheric evaporative demand (vapour pressure deficit; VPD) that interact with both the Ψsoiland transpiration rate to determine plant hydration. VPD can strongly modulate the dynamics of Pstem independently of Ψsoil, hence influencing the gradient between Ψstemand Ψsoil. Increases in VPD typically cause the plant to transpire more water resulting in lower Ψstemunder constant Ψsoil. Therefore, failure to consider the interactive effects of VPD and Ψsoilon Ψstemregulation will give an incomplete representation of the overall drought response.

[0006] Characterising plant hydration is particularly important for agricultural applications. Overwatering crops can cause rot, cracking in fruit, and wasted water runoff, all of which can result in lower crop yields and lower profit margins. Underwatering crops can lead to decreased crop quality and yield, and soil degradation.

[0007] Therefore, there exists a need for an improved method of characterising the pressure regulatory behaviour of plants. The solution would ideally capture the pressure regulatory behaviour of plants in response to VPD and Ψsoil.SUMMARY OF THE INVENTION

[0008] Disclosed herein is a method of quantifying the pressure regulatory behaviour in a plant (e.g., a plurality of plants, e.g., a plurality of plants comprising different plant species), whereby the method includes the monitoring of plant vascular water potential (Ψstem) under at least two different atmospheric dryness (VPD) conditions and at least two different soil water availability (Ψsoil) conditions.

[0009] In an embodiment, the monitoring of plant vascular water potential is in-situ or ex-situ.

[0010] Disclosed herein is a method of quantifying the pressure regulatory behaviour in a plant (e g., a plurality of plants, e.g., a plurality of plants comprising different plant species),whereby the method includes the monitoring of an in-situ plant vascular water potential (Ψstem) under at least two different atmospheric dryness (VPD) conditions and at least two different soil water availability (Ψsoil) conditions.

[0011] Disclosed herein is a method for determining water-use regulatory behaviour in a plant (e.g., a plurality of plants, e g., a plurality of plants comprising different plant species) including recording atmospheric evaporative demand (VPD), soil water potential (Ψsoil), and stem water potential (Ψstem).

[0012] In an embodiment, stem water potential is measured using an invasive or non-invasive measurement device.

[0013] Suitable measurement devices would be known to persons skilled in the art, illustrative examples of which include, but not limited to, measurement devices for monitoring and / or recoding the atmospheric and physical properties (e.g., volumetric changes) of plants, such as invasive dendrometers (e.g., point dendrometers) and non-invasive dendrometers (e.g., fibre-optic sensing dendrometers and the optical dendrometer described elsewhere herein). Suitable measurement devices also include devices that provide direct measurements (e.g., direct measurements of plant water potential, e.g., pressure chamber, psychrometric devices or tensiometers), and those that provide indirect measurements, such as invasive dendrometers (e.g., point dendrometers) and non-invasive dendrometers (e.g., fibre-optic sensing dendrometers and the optical dendrometer described elsewhere herein).

[0014] As used herein, the term “monitoring” may be understood to relate to collecting data that is a directly related to a particular quantity, or data that provides a proxy for that particular quantity. For example, monitoring plant vascular water potential may include collecting direct measurements of plant vascular water potential, or it may include collecting data that can be used as a proxy for vascular water potential (e.g., data that exhibits similar behaviour to vascular water potential that can be used to infer the behaviour of vascular water potential). In the latter case, a known or calculated correlation may be used to infer the behaviour of vascular water potential. Using a proxy for monitoring the vascular water potential might require some calibration of the device used to measure the quantity to be used as a proxy.

[0015] As used herein, the term “recording” may be understood to relate to collecting and storing data (e.g., in computer memory or storage) that is a directly related to a particular quantity, or data that provides a proxy for that particular quantity. For example, recording plant vascularwater potential may include collecting and storing direct measurements of plant vascular water potential, or it may include collecting and storing data that can be used as a proxy for vascular water potential (e.g., data that exhibits similar behaviour to vascular water potential that can be used to infer the behaviour of vascular water potential). In the latter case, a known or calculated correlation may be used to infer the behaviour of vascular water potential. Using a proxy for monitoring the vascular water potential might require some calibration of the device used to measure the quantity to be used as a proxy.

[0016] In an embodiment, stem water potential is measured using a non-invasive measurement device.

[0017] In an embodiment, the non-invasive measurement device produces an indirect measurement of stem water potential.

[0018] In another embodiment, the non-invasive measurement device is an optical dendrometer.

[0019] Also disclosed herein is a method for determining water-use regulatory behaviour in a plant (e g., a plurality of plants, e.g., a plurality of plants comprising different plant species) including recording atmospheric evaporative demand (VPD), soil water potential (Ψsoil), and stem water potential (Ψstem), wherein stem water potential is measured using a non-invasive measurement device.

[0020] In an embodiment, the non-invasive measurement device is a non-invasive dendrometer.

[0021] In an embodiment, the non-invasive measurement device is an optical dendrometer.

[0022] In an embodiment, the method further includes a step of determining irrigation requirements of the plant.

[0023] Beneficially, the determination of the irrigation requirements of the plant allows for the optimisation of an irrigation schedule for the plant (or a plurality of plants). In some embodiments, optimisation of an irrigation schedule may be used to affect the properties of the plant for a specific purpose (e.g., increasing yield, controlling growth / maturation (e.g., pit hardening, maturation index), reduction of post-harvest water loss, fruit quality, fruit size, skin thickness (e g., tuber skin thickness), maintaining consistency in shape, and minimising defects).For example, optimisation of an irrigation schedule for a grape vine (or a plurality of grape vines) may be used induce water-induced stress conditions to affect the resulting yield and characteristics of the grapes produced by the grape vine (e.g., taste, aroma, sugar content, etc ).

[0024] Accordingly, in some embodiments, the method further comprises repeating the method at one or more different time points to determine the pressure regulatory behaviour or water-use regulatory behaviour of the plant and comparing the determined pressure regulatory behaviour or water-use regulatory behaviour at a first time point with the determined pressure regulatory behaviour or water-use regulatory behaviour at one or more subsequent time points to determine if there has been a change in the irrigation requirements of the plant over time.

[0025] In another embodiment, the method further comprises comparing the determined pressure regulatory behaviour or water-use regulatory behaviour of the plant with a reference value.

[0026] In an embodiment, the reference value is representative of pressure regulatory behaviour or water-use regulatory behaviour in a plant or a population of plants. In an illustrative example, the comparison may be carried out using a reference value that is representative of a known or predetermined irrigation schedule to produce a plant with specific properties. In another illustrative example, the comparison may be carried out using a reference value that is representative of a known or predetermined developmental stage (e g., vegetative growth phase or reproductive growth phase). In another illustrative example, the comparison may be carried out using a reference value that is representative of healthy plant. In yet another illustrative example, the comparison may be carried out using a reference value that is representative of plant with abnormal plant hydraulics, e.g., a diseased plant. Persons skilled in the art will appreciate that were the comparison is carried out using a reference value that is representative of plant with abnormal plant hydraulics, the methods disclosed herein may be useful for detecting plants with abnormal plant hydraulics, e.g., a plant with root disease.

[0027] In an embodiment, the reference value is a soil hydration threshold, wherein plant activity becomes limited below the soil hydration threshold. In an embodiment, the soil hydration threshold coincides with a predetermined soil water potential (Ψsoil) value known to cause a decline in soil-plant hydraulic conductance.

[0028] In an embodiment, the method further includes a step of determining a hydration level of the plant.

[0029] In an embodiment, the method further includes a step of determining a daily stress exposure measurement for the plant.

[0030] In an embodiment, the method further includes a step of optimising an irrigation schedule of the plant in order to optimise a yield of said plant.

[0031] In an embodiment, the method further includes a step of recording seasonal exposures and environmental conditions.

[0032] In an embodiment, the method further includes a step of determining a soil hydration threshold, wherein plant activity becomes limited below the soil hydration threshold.

[0033] In an embodiment, the soil hydration threshold coincides with a value of soil water potential (Ψsoil) known to cause a decline in soil-plant hydraulic conductance.

[0034] In an embodiment, the non-invasive measurement device is attached to a leaf or a nongrowing branchlet. The branchlet may be less than approximately three millimetres in diameter.

[0035] In an embodiment, the stem water potential is recorded as daily maximum and minimum values. In other embodiments, the stem water potential is measured with high temporal resolution, for example, the period between measurements may be of the order of minutes. In particular examples, the stem water potential may be measured in from approximately 15 to approximately 30 minute intervals. In other embodiments, the stem water potential may be measured with an interval in the range of from approximately 5 to approximately 60 minutes.

[0036] In an embodiment, the non-invasive measurement device comprises an optical dendrometry device.

[0037] In an embodiment, the recording of atmospheric evaporative demand and stem water potential are expressed as single daily maximum values.

[0038] In an embodiment, the method further includes a step of determining a vascular pressure phenotype, also referred to herein as a “hydraulic phenotype”. The hydraulic phenotype may be used to describe the whole plant regulation of hydration, including nocturnal and daytime behaviour. Additionally, or alternatively, the hydraulic phenotype may be used to quantitatively compare vulnerability between species or genotypes.

[0039] In an embodiment, the method further includes a step of inferring whole plant stomatal responses to the combined effect of atmospheric evaporative demand and soil water potential.

[0040] As detailed elsewhere herein, the methods and systems of the present invention are generalizable across all plants, that is, the parameters of the model disclosed herein are based on physical properties that are measurable across all plants, independent of species or genotype.

[0041] In an embodiment, the plant is not a woody species.

[0042] In an embodiment, the plant is an agricultural crop plant species. In another embodiment, the agricultural crop plant species is an agricultural grain crop species, such as wheat, barley and canola.

[0043] In an embodiment, the plant is a tree.

[0044] In an embodiment, the plant is an herbaceous species.

[0045] In an embodiment, the plant is a fruit bearing plant species, such as a cherry tree, grape vine, or an herbaceous fruit bearing plant species, e.g., a tomato plant.

[0046] In an embodiment, the method quantifies the pressure regulatory behavior in a plurality of plants. In an embodiment, the plurality of plants comprises plants the same plant species. In another embodiment, the plurality of plants comprises plants of different plant species.

[0047] In an embodiment, the method determines water-use regulatory behaviour in a plurality of plants. In an embodiment, the plurality of plants comprises plants the same plant species. In another embodiment, the plurality of plants comprises plants of different plant species.

[0048] In an embodiment, the method further includes a step of comparing water-use regulatory behaviour between different plant species.

[0049] Also disclosed herein is a method of monitoring the xylem pressure in a plant (e.g., a plurality of plants, e.g., a plurality of plants comprising different plant species) through the regulation of stomatal transpiration, said monitoring method including a step of calibrating sensor outputs from a plant to yield species-specific information about plant water usage.

[0050] Also disclosed herein is a method of monitoring the water use of a plant (e.g., a plurality of plants, e.g., a plurality of plants comprising different plant species).

[0051] Also disclosed herein is a method of regulating stomatal transpiration in a plant (e.g., a plurality of plants, e.g., a plurality of plants comprising different plant species). This method may be used to regulate water status and therefore plant stress and transpiration by coupling this information to irrigation scheduling.

[0052] Also disclosed herein is a method for characterising stomatal-driven pressure regulation in a tree, said method including a step of building species-specific pressure response profiles by integrating pressure and environmental data to create one or more three-dimensional plots representing said data. The method may further include a step of predicting pressure as a function of atmospheric conditions (e.g., VPD) and soil hydration.

[0053] Also disclosed herein is a system for characterising water-use regulatory behaviour in a plant (e.g., a plurality of plants, e.g., a plurality of plants comprising different plant species), comprising: a device for measuring atmospheric evaporative demand (VPD); a sensor for measuring soil water potential (Ψsoil); and an instrument for measuring plant vascular water potential (Ψstem), wherein data recorded by each of the device, the sensor, and the instrument are combined to determine a stress level of the plant.

[0054] In an embodiment, the data are used to determine a hydraulic phenotype of the plant.

[0055] In an embodiment, the system may further comprise a processor for determining irrigation requirements of the plant.

[0056] In an embodiment, the system may further comprise an irrigator for irrigating the plant.

[0057] In an embodiment, the instrument is a non-invasive instrument.

[0058] In an embodiment, the data are used to determine irrigation requirements of the plant.

[0059] In an embodiment, the data are used to determine a hydration level of the plant.

[0060] In an embodiment, the data are used to determine a daily stress exposure measurement for the plant.

[0061] In an embodiment, the data are used to determine an irrigation schedule for the plant.

[0062] In an embodiment, the system further includes a database of seasonal exposure and environmental conditions.

[0063] In an embodiment, the instrument is attached to a leaf or a non-growing branchlet. The branchlet may be less than approximately three millimeters in diameter.

[0064] In an embodiment, the plant vascular water potential is recorded as daily maximum and minimum values. In other embodiments, the plant vascular water potential is measured with high temporal resolution, for example, the period between measurements may be of the order of minutes. In particular examples, the plant vascular water potential may be measured in from approximately 15 to approximately 30 minute intervals. In other embodiments, the plant vascular water potential may be measured with an interval in the range of from approximately 5 to approximately 60 minutes.

[0065] In an embodiment, the instrument comprises an invasive or non-invasive measurement device.

[0066] In an embodiment, the instrument comprises a non-invasive measurement device.

[0067] In an embodiment, the non-invasive measurement device produces an indirect measurement of stem water potential.

[0068] In an embodiment, the instrument comprises an optical dendrometry device.

[0069] In an embodiment, the device for measuring atmospheric evaporative demand the instrument for measuring plant vascular water potential record single daily maximum values.

[0070] In an embodiment, the data are used to determine a vascular pressure phenotype, (which may also be referred to herein as an “hydraulic phenotype”). The hydraulic phenotype may be used to describe the whole plant regulation of hydration, including nocturnal and daytime behaviour. Additionally, or alternatively, the hydraulic phenotype may be used to quantitatively compare vulnerability between species or genotypes.

[0071] In an embodiment, the data are used to infer whole plant stomatal responses to the combined effect of atmospheric evaporative demand and soil water potential.

[0072] In an embodiment, the plant is not a woody species.

[0073] In an embodiment, the plant is an agricultural crop plant species. In another embodiment, the agricultural crop plant species is an agricultural grain crop species, such as wheat, barley and canola.

[0074] In an embodiment, the plant is a tree.

[0075] In an embodiment, the plant is an herbaceous species.

[0076] In an embodiment, the plant is a fruit bearing plant species, such as a cherry tree, grape vine, or an herbaceous fruit bearing plant species, e.g., a tomato plant.

[0077] In some embodiments, the data are used to compare water-use regulatory behaviour between different plant species.

[0078] While components and method steps will be described below for use in combination with each other in the preferred embodiments of the present invention, it is to be understood by a skilled person that some aspects of the present invention are equally suitable to be used interchangeably between one or more embodiments of the present invention and / or suitable for use as standalone inventions that can be individually incorporated into other devices and methods not described herein.

[0079] In the description, reference to positional descriptions, such as lower and upper, or inner and outer, are to be taken in context of the embodiments depicted in the figures, and are not to be taken as limiting the invention to the literal interpretation of the term but rather as would be understood by the skilled addressee.

[0080] The articles "a", "an" and "the" include plural aspects unless the context clearly dictates otherwise. Thus, for example, reference to "an activity" includes a single activity, as well as two or more activities; reference to "a plant" includes a single plant, as well as two or more plants (i.e., a plurality of plants, e.g., a plurality of plants comprising different plant species) and so forth.

[0081] The terms “about” or “approximately” when used in relation to a stated reference point for a quality, level, value, number, frequency, percentage, dimension, location, size, amount, weight or length may be understood to indicate that the reference point is capable of variation, and that the term may encompass proximal qualities on either side of the reference point.

[0082] As used herein, the term "substantially" may be used merely to indicate an intention that the term it qualifies should not be read too literally and that the word could mean “sufficiently”, “mostly” or "near enough”.

[0083] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this inventionbelongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, a limited number of the example methods and materials are described herein.

[0084] In the description in this specification, reference may be made to subject matter which is not within the scope of the appended claims. That subject matter should be readily identifiable by a person skilled in the art and may assist in putting into practice the invention as defined in the presently appended claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The invention will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which:

[0086] Figure 1 is a time-series of diurnal variation in daily precipitation, vapor pressure deficit (VPD), and in-situ measurements of stem water potential (stem) monitored at 15-30 min interval with optical dendrometers in four individual trees of Callitris rhomboidea across multiple growing seasons over four year-period.

[0087] Figure 2A is a plot showing the response of diurnal changes in ΔΨ to diurnal VPD and Ψsoil between 11:00h and 16:00 h in one tree (Tree 1 shown in Figure 1) of C. rhomboidea monitored over two highly variable growing seasons.

[0088] Figure 2B is a plot showing an example of diurnal variation of Ψstemin response to changes in diurnal VPD over one day in one representative tree of C. rhomboidea under wet (black closed circle, upper series) and dry soil conditions (orange closed circle, lower series).

[0089] Figure 2C is a plot showing the variation in VPD for the data shown in Figure 2B.

[0090] Figure 3 is a plot showing the response of daytime maximum soil to stem water potential gradient (ΔΨ = Ψsoil - Ψstem) to its corresponding maximum daytime VPD under variable soil water potentials (Ψsoil) monitored continuously and in-situ in four individual trees of C. rhomboidea across multiple highly variable growing seasons over four years.

[0091] Figure 4A is a plot showing daytime maximum ΔΨ as a function of VPD and Ψsoil in one representative tree of C. rhomboidea from one growing season, the solid line curves show the expected value of the modelled response.

[0092] Figures 4B and 4C are plots showing the change in the mean (black line) and 200 posterior samples (yellow lines) of the asymptote θ and the rate λ of the asymptotic relationship between ΔΨ and VPD as Ψsoil becomes more negative due to soil drought.

[0093] Figure 5 is a plot showing a 3D modelled surface response of ΔΨ to VPD andin four trees of C. rhomboidea monitored across multiple growing seasons over a four-year period — the Ψsoilis a log4scale.

[0094] Figure 6 is diagram showing posterior estimates of the five key parameters of the proposed hierarchical model for ΔΨ response to VPD and Ψsoil in C. rhomboidea monitored across multiple highly variable growing seasons — the points are the posterior mean values, the thick and thin lines are the 50% and 95% credible intervals, respectively, and the blue shaded areas are the posterior density distributions.

[0095] Figure 7 is a plot showing a surface response of whole plant diffusive conductance (gc) as a function of Ψsoiland VPD predicted from the modelled response of maximum daily ΔΨ to Ψsoil and VPD in C. rhomboidea. gcwas predicted using a combination of Darcy's law and Fick's law of diffusion — the Ψsoilaxis is a log4scale.

[0096] Figure 8 is a plot showing the relationship between branchlet width monitored in the field with optical dendrometry and corresponding TΨstemmeasured on neighbouring covered nontranspiring branchlets with a pressure chamber in four trees of C. rhomboidea over multiple growing seasons.

[0097] Figure 9A is a plot showing TΨstem, Tson and VPD as a function of time.

[0098] Figure 9B is a plot showing a zoomed view of a period shown in Figure 9A.

[0099] Figure 10 is a plot showing the response of diurnal changes in ΔΨ to corresponding changes in diurnal VPD and Ψsoilbetween 11:00h and 16:00 h in four trees of C. rhomboidea monitored over multiple highly variable growing seasons over a four-year period.

[0100] Figure 11 provides plots showing a distribution of the modelled ΔΨ residuals against both Ψsoil (Figure 11A) and VPD (Figure 11B) in four different trees of C. rhomboidea monitored across multiple growing seasons and diverse seasonal conditions of VPD and Ψsoil.

[0101] Figure 12 is a plot showing comparisons between the observed and predicted ΔΨ values in four trees of C. rhomboidea monitored across highly variable growing seasons.

[0102] Figure 13 provides plots of posterior estimates by plant and year for the asymptote θ0 (Figure 13A) and rate λ0 (Figure 13B) under relatively hydrated soil the points are the posterior mean values, the thick and thin lines are the 50% and 95% credible intervals, respectively, and the dashed line is the mean estimate of the parameters.DETAILED DESCRIPTION

[0103] The present invention is predicated, at least in part, on observations made in experiments performed to investigate water-use regulatory behaviour in plants using measurements of one or more of (i) climate parameters (e.g., temperature and humidity), (ii) soil conditions (e.g., soil moisture levels), and (iii) plant hydration levels. Advantageously, obtaining measurements of these parameters is not dependent on any specific species or genotype of a plant. Therefore, the methods and systems described herein are based on physical properties that are measurable across all plant species. Embodiments of the present invention allow for the identification of a threshold for a given species of plant where declining soil water content starts to limit plant activity. In other words, the method can be used to identify a level of soil water availability below which plant activity (e.g., gas exchange) becomes limited.

[0104] Preferred embodiments of the present invention provide a method for determining how vapour pressure deficit (VPD) and soil water potential (Ψsoil) interact with plant behaviour to regulate plant hydration during a growing season. Embodiments of the invention provide a single mechanism-sensitive model that captures the xylem pressure regulatory behaviour of a tree species in response to VPD and Ψsoilover multiple highly variable growing seasons. To test the model, fine scale (15-30 minute interval) time series measurements of in situ Ψstem were collected by optical dendrometry in field grown specimens of the conifer species Callitris rhomboidei. Advantageously, embodiments of the invention can determine a hydraulic phenotype that captures the whole plant regulation of hydration.

[0105] The regulation of plant vascular water potential (Ψstem) is one of the most dynamic and important behaviours in vascular plants, playing a central role in both gas exchange rates as well as vulnerability to drought. Despite this, the characterization of plant pressure regulatory behaviour in response to combinations of soil and atmospheric water deficit remains elusive. Embodiments of the present invention seek to alleviate this limitation by proposing a method for characterising pressure regulation caused by stomatal responses to atmospheric (VPD) and soil water deficits (Ψsoil) using a minimal set of species-specific regulatory parameters.

[0106] The method was tested using in situ measurements of Pstem monitored continuously across multiple highly variable growing seasons in mature trees of a hardy conifer Callitris rhomboidea. Embodiments of the invention reveal a highly predictable regulatory behaviour of Ψstem with 74% of variance explained by Ψsoiland VPD alone. The five mechanism-aligned parameters estimated by the model constitute a vascular pressure phenotype that quantifies the sensitivities and thresholds for water-pressure regulation mediated by stomatal control in C. rhomboidea. Advantageously, embodiments of the present invention provide a method for characterising and comparing water-use regulatory behaviour among species, as well as improving understanding of plant carbon and water use.

[0107] The data presented in the figures were collected from experiments carried out in Pelverata located on the southeast of Tasmania, Australia (43°03'03.5"S 147°06'15.8"E, 250 m elevation). The local climate is characterized by cool, wet winters and relatively mild summers with episodic rainfall events and maximum daily air temperature ranging between 22°C and 30°C. The soil on the site consists of well-drained, clay loam soils. As would be understood by a person skilled in the art, the methods and techniques described herein are applicable to environments, conditions and locations that are different to those in which the data were collected

[0108] The experiments that were conducted to capture the data are further described below.Continuous monitoring of Pstem

[0109] Ψstem was continuously monitored on four 12-year-old C. rhomboidea trees (5 m tall) using optical dendrometers over multiple growing seasons across a four-year period between 2021 and 2024. The trees were separated by an average distance of 50 m. In each tree, an optical dendrometer was attached to a determinate non-growing branchlet (<3 mm in diameter) to continuously monitor width changes at 15-30 min intervals. Branchlet width was calibrated in each individual tree against Ψstem measured periodically (every 15-20 days) during the study period with a Scholander pressure chamber (PMS Instruments, Corvallis, OR, USA). These measurements were performed on neighbouring non-transpiring leaves that were wrapped in a damp paper towel and aluminium foil for at least 1 h before measurements. The leaves were then excised with a razor blade and immediately wrapped in a damp paper towel and placed in sealable bag with moist paper towels. They were then put in a dark box, transported to the lab, and measured within 1 hour after the leaves were collected. Plant-specific relationships between branchlet width and measuredΨstem using the pressure bomb were established and used to determine Ψstemdynamics during the experimental period (Figure 8).

[0110] In some embodiments, the monitoring methods described herein advantageously do not require calibration. In accordance with these embodiments, the methods disclosed herein can be carried out using direct or indirect measurements of plant water potential. In some embodiments, the soil water limiting threshold can be used as a proxy for plant water potential. In another embodiment, optical dendrometry measurements may be used as a proxy for the plant vascular water potential (Ψstem). Optical dendrometry measurements using the device disclosed herein may include measurements of volumetric changes in a plant by proxy. For example, these measurements can include taking images (e.g., with an optical camera) of a plant or a part thereof, and measuring the size of the same at different time points. Changes in the size of the plant or part thereof can then be used as an indication of the plant water potential. Additionally or alternatively, the measurements of the volumetric changes in the plant can be used to calculate the water potential using a known or derived correlation. In other words, raw data from measurements of the volumetric changes in the plant can be used in the methods and systems disclosed herein. Alternatively, calculated or directly measured values of water potential can be used in the methods and systems disclosed herein.

[0111] The examples provided herein include data from optical dendrometers. As would be understood by a person skilled in the art, similar data could be obtained using other measurement techniques, including both invasive (e.g., methods that damage a plant) and non-invasive (e.g., methods that do not damage a plant) techniques described elsewhere herein.

[0112] Ψstem measurements were paired with site-level air temperature and humidity that were monitored continuously at 15-30 min intervals throughout each season using an SHT31 weatherproof temperature and humidity sensor installed within the mid-canopy of one of the trees. These measurements were used to calculate VPD. Rainfall data were obtained from the Groove weather station maintained by the Australian Bureau of Meteorology (Station ID: 94220) located approximately 6 km from the field.Modelling the response of maximum daily ΔΨ to VPD and Ψsoil

[0113] To capture the long-term variation in the soil to branchlet pressure gradient (ΔΨ) regulation in response to change in VPD and Ψsoil, a model of ΔΨ using a single maximum daytime ΔΨ to represent each day was constructed. For each individual tree, maximum daily ΔΨ wascalculated as the difference between minimum mean daily Ψstem (most negative value) averaged over 2-hour period (using 4-8 datapoints) and its corresponding daytime Ψsoil(Figures 9A and 9B). The corresponding daytime Pson was determined using linear interpolation from the optically derived mean predawn PΨstem(maximum value of Pstem before sunrise) averaged over 2 hours. Under conditions of significant nocturnal transpiration predawn Ψstem does not represent Ψsoildue to incomplete equilibration (Sellin, 1999; Kavanagh et al., 2007; Kangur et al., 2020). For this reason, predawn Ψstemvalues used for interpolating daytime Ψsoilthroughout the study period were selected on the nights where humidity remained above 95% (VPD <0.05 kPa) throughout the whole night (between midnight and before sunrise) (Figures 9A and 9B). Under such conditions, night-time transpiration was found to be negligible in this species, hence predawn PΨstemwas assumed to have been given sufficient time to establish equilibrium with Pson in the rooting zone.

[0114] The dynamic regulation of maximum daytime A P was modelled in terms of seasonal variation in both VPD and Ψsoilfrom a total of 571 days from four trees over four years to assess whether a common behaviour emerged between trees. Based upon the principles of Darcy’s law explaining water flow and generic stomatal regulation behaviour in plants, together with the observed trends in the data, the following exponential trend was proposed for the relationship between AT and VPD, for which a statistical model can be presented as:where ε ~N(0,σ²) is the distribution of residual errors. The shape of this two-parameter curve ranges from an approximately linear trend (low λ, low sensitivity of stomata to VPD) to a highly non-linear relationship characterised by a rapid initial increase followed by stabilisation (high λ, high sensitivity of stomata to VPD). Therefore, this model is flexible and is able to accommodate the different classes of stomatal responses (and the resulting A P regulation) to VPD observed in different species.

[0115] A representative set of curves for the studied species is shown by the lines in Figure 4A where the positive-valued parameters θ and λ characterise the asymptote and rate of curvature, respectively.

[0116] Next, the base model shown in Equation (1) was extended to allow both θ and λ to depend on Ψsoilas follows:and

[0117] These models set θ and λ to the estimated (constant) values θ0and λ0, respectively, for values of Ψsoilabove (less negative than) the estimated soil water potential threshold (Ψsoil_threshold) and specify an exponential change thereafter for increasing negative values of Ψsoil. Under increasing drought conditions, every plant will eventually close its stomata completely, therefore the model for θ must tend toward zero at very negative Ψsoil. However, a continuous decline in θ as Ψsoilbegins to decline from the saturated conditions is not expected. Rather, it is likely that there exists a range of Ψsoilconditions where θ and λ are effectively constant, characterising a stable relationship between ΔΨ and VPD. It is important to note that while the model allows for a threshold in Ψsoil, it also includes the possibility of no threshold as a limiting case and thus tests the hypothesis of a stable relationship between ΔΨ and VPD under changing Ψsoil. The reason for using an exponential decay in Equations (2) and (3), rather than say a linear trend, is to ensure that both θ and λ remain positive. For small values of Ψthreshold − Ψsoil, α and β, the trends are, in any case, approximately linear. Examples of these trends for θ and λ are shown in Figures 4B and 4C, respectively, where the rate α is positive valued (i.e., θ decreases below the threshold) and the rate β is negative valued (i.e., λ increases below the threshold).

[0118] Finally, to allow us to include observations for different plants across different growing years, a hierarchical structure was used to estimate θ0and λ0as correlated normally distributed random effects with separate levels for each plant-year combination.

[0119] The model was fit with the R package brms for full Bayesian inference using Hamiltonian Monte Carlo sampling methods. The estimated posterior comprised 4 chains of 2000 iterations and model convergence was established using the improved R statistic and visual inspection of the chains. Further diagnostics included plots of the residuals against both VPD and Ψsoil and posterior-predictive plots which are provided in Figures 11A, 11B and 12.Modelling gcbehaviour using the modelled response of ΔΨ to VPD and Ψsoil

[0120] Stomata respond to Psou and VPD via the changes in PΨstemthey induce. However, stomatai behaviour has typically been studied as a function of these factors, independently. Under non-limiting light and soil moisture conditions, changes in VPD can be the main factor influencing stomatal behaviour. However, during soil drying, stomata can be influenced by the combined effect of both VPD and Ψsoil. Hence failure to account for the interactive effect of both factors will give an incomplete picture of stomata behaviour under continually changing conditions of both Ψsoiland VPD. Given that stomata are the main regulator of PΨstemdetermining the magnitude of A *P, their behaviour can be inferred from A *P regulation. Here, the response of the whole plant diffusive conductance (gc) to both VPD and Ψsoilwas modelled from the modelled response of maximum daily ΔΨ to ΨsoilVPD using a combination of Darcy's law and Fick's law of diffusion as follows:Ks-p× ΔΨ (VPD, Ψsoil)gc∝ ―――――, (4)where Ks-pis the soil-to-plant hydraulic conductance and was set constant across the entire range of Ψsoil experienced by the trees. In the studied species, there is evidence that Ks-premains stable during the early stages of soil drying until Ψsoil reaches approximately -1 MPa then declines thereafter (Bourbia et al., 2021). However, the effect of this decline would not impact the Ψsoilthreshold of declining gc, but only cause an increase in the slope of declining gcbelow the Ψsoilthreshold. Hence Ks-pwas kept constant to more clearly focus the model on the contribution of variation in VPD and Ψsoilon the modelled gcregulation. Inclusion of a declining K at -1MPa only had the effect of increasing the slope of the response of gcto Ψsoil, but it did not change thresholds. gcwas normalized to the maximum value and expressed as a percentage of maximum.RESULTS DISCUSSION

[0121] In order to test the methods for characterising pressure regulation in whole plants in response to VPD and Ψsoilover multiple seasons and years it was necessary to collect a substantial data series that faithfully recorded Ψstemat high temporal resolution. As shown in Figure 1, the data collected from four trees over four years using optical dendrometers provided the necessary dataset, with approximately 130,000 individual records of Ψstemcollected between 2021 and 2024. Throughout the study period, the monitored trees exhibited strong variation in diurnal PΨstemand experienced a wide range of VPD and Pson (predawn Pstem) that was associated with seasonalvariation in VPD and episodic drought and rainfall events (Figure 1). Different trees appeared to have had access to different soil moisture levels during soil drying, as shown by differences in Pson experienced throughout each study period likely due to variability in root depths between trees (Figure 1). For instance, during the growing season of 2024, the monitored trees were all hydrated to the same level (Ψsoilclose to 0 MPa) in the beginning of the season, but as the drought progressed, Ψsoilexperienced by the trees diverged considerably, ranging from -2.6 to -3.6 MPa during the driest period. A degree of variation in root depth or local soil water supply was expected considering that trees were separated by distances of 50 m and soil depth may have been variable. The trees also experienced different soil moistures between different years. For example, year 2023 was exceptionally wet compared to other years as predawn Ψstemremained high and above -1 MPa throughout the entire season. This heterogeneity in the soil water availability within and between years provided an additional source of variation that provided an ideal test of the general behavioural model for the species.Diurnal regulation

[0122] Analysis of diurnal ΔΨ was restricted to the daytime periods between 11:00 and 15:00 h, where stomata opening was not impacted by light limitation or the circadian effects due to time of day, hence the dynamics of ΔΨ were expected to be influenced mostly by the response of stomata to VPD and Ψsoil. Diurnal patterns of pressure variation were highly consistent between individuals (Figure 2A and Figure 10). The within day patterns of pressure variation exhibited a saturating relationship between ΔΨ and VPD, where ΔΨ increased with increasing VPD then approached saturation as VPD exceeded 1 kPa (Figure 2A and Figure 10). Such a relationship would be expected if stomatal conductance responded with the typical 1 / VPD dependence expected under most stomatai control models (Oren et al., 1999; Grossiord et al., 2020). This relationship, however, appears to be influenced by Pson as well, as evidenced by a substantial reduction in the magnitude of ΔΨ response to VPD under dry soils (more negative Ψsoil) compared to well hydrated soils (Ψsoilclose to 0 MPa) (Figures 2B and 2C).

[0123] To investigate these patterns further, the time frame of observation was changed from minutes to days, using only one steady state daily maximum A P per day.

[0124] When AP response to Psou and VPD was expressed in terms of a single daily maximum, all trees showed a very similar trend to that observed using the full daily time series (Figure 3). These daily maxima, a total of 571 data points of maximum daily A P monitored acrossfour different trees and multiple growing seasons were used to create the behavioural model. Hereafter, ΔΨ and VPD will refer to their steady state maximum daily values unless stated otherwise. Steady state daily maxima instead of diurnal ΔΨ was chosen to avoid the potential confounding effect of capacitance on diurnal ΔΨ dynamics under non-steady state conditions of diurnal VPD, and to compare longer term responses.Relationship between A T and VPD under wet conditions

[0125] Under wet soil conditions (Ψsoilclose to 0 MPa), the modelled relationship, based on Equations (1), (2), and (3), between daily maximum ΔΨ and VPD was characterised by an initial increase (at rate λ0) before approaching an asymptote (θ0), with ΔΨ attaining 95% of its asymptotic value when the VPD reached 1.94 kPa (1.53, 2.67; 95% credible interval [CI]) (Figures 4A and 5). This trend was also observed in diurnal variation in A T (Figure 2A) and mirrors the commonly observed relationship between sap flux and VPD, consistent with a hydraulic model whereby stomatai control exerts a limit on transpiration as leaf water potential decreases. This asymptotic relationship suggests that stomata were progressively closing as VPD increased preventing further declines in TΨstem, thus constraining A T outside the range of hypertension, but at the expense of reduced carbon gain.Fixed relationship between and VPD in the initial stages of soil drying

[0126] Examining the VPD relationship with ΔΨ over multiple and highly variable growing seasons enabled the effect of declining Ψsoilon the VPD response to be examined. As the soil progressively dried (Ψsoilbecame more negative), the relationship between ΔΨ and VPD initially remained constant (Figure 5), as characterised by the constancy of θ and λ with declining Ψsoil(Figures 4B and 4C). This suggests that stomatai regulation of AT in response to VPD was insensitive to the initial decline in Tson and that the regulatory function that produced the asymptotic response between AT' and VPD initially remained unchanged despite a decline in Tsou. Such apparent non-sensitivity of stomatal response to declining Ψsoilmight be attributed to a gradual shift in the osmotic potential of guard cells or mesophyll cells to more negative values during the initial stages of soil drying, a behaviour that has been observed in many species drying slowly in the field and described as isohydrodynamic behaviour. Maintaining a constant ΔΨ~VPD relationship may serve to maintain soil water extraction and extend carbon acquisition as the soil dries, but at the expense of declining Ψstemand risk of hypertension. However, the maintenance ofconstant ΔΨ at high VPD by stomatal closure paradoxically restricts water uptake and carbon gain which can place the plant at a disadvantage in competitive environments where water is limited.Changing relationship between ΔΨ and VPD below a Ψsoilthreshold

[0127] Despite the strong conservation in the relationship between maximum daily ΔΨ and VPD under most levels of soil hydration, a clear threshold was observed as Ψsoildecreased below -0.83 MPa (-0.939, -0.731; 95% CI) (Figure 6), whereupon all trees became highly sensitive to both Ψsoiland VPD. Beyond this threshold, a strong reduction in the ΔΨ asymptote (or ΔΨ limit; θ) (Figure 4B) with a rate a = 0.29 (0.26, 0.32; 95% CI) (Figure 6) was observed. During this phase of strong limitation of ΔΨθ collapsed by more than 50% as Ψsoilreached -4 MPa (Figure 5). The sensitivity of ΔΨ to VPD also increased, with ΔΨ attaining the asymptote at progressively lower VPD as exhibited by the progressive increase in λ. (Figure 4C), with a rate P = -0.22 (-0.4, -0.06; 95% CI) (Figure 6), as Ψsoildeclined below the threshold (Figure 5). These relationships indicate a pronounced stomatai regulation of Ψstemin response to both Ψsoiland VPD and suggest a limit to further osmotic adjustment beyond this soil hydration threshold. Although the mechanism triggering such strong stomatai control below this Ψsoilthreshold remains unclear, it may be caused by the triggering of abscisic acid (ABA) production to strongly reduce guard cell turgor. The estimated Ψsoilthreshold appears to coincide with the value of Ψsoilknown to cause a steep decline in soil-plant hydraulic conductance (Ks-p) in this species (Bourbia et al., 2021), while also approximating the point where soil hydraulics become highly limiting for water extraction by plants.

[0128] In some embodiments, AW is calculated using the stem water potential (WΨstem). In accordance with this embodiment, the modelled surface is dependent only on the plant, rather than the plant in combination with the soil type (e.g., soil composition and / or condition). The ability to rely on the plant alone is based on the observation that stomatai regulation can occur independently of soil hydraulic limitation, and thus, uniform behaviour is expected within a given plant species irrespective of soil type.Predictability of Ψstemregulation under seasonal changing in VPD and Ψsoil

[0129] Recent reports of Ψstemmonitoring suggest that temporal dynamics of ΔΨ in response to changes in Ψsoilare highly unpredictable even within individuals of the same species between different seasons, casting doubt on the ability of Wstem to characterise and predict the behaviour of species. This motivated the inventors to determine whether a physiologically-informed model, thatincorporates the regulatory patterns observed above in response to the joint effects of both VPD and Ψsoil, could accurately predict temporal changes in ΔΨ observed in all trees across all seasons. The present Bayesian model included data from different plants and different years but allowed sufficient flexibility to accommodate plant and year specific variation. Using this model, it was found that 73% of the variance in maximum diurnal ΔΨ monitored in different individuals across highly variable growing seasons (Figure 1) could be explained using only Ψsoiland VPD as the dependent variables. This result highlights the importance both these variables hold in modulating water potential of the whole plant. The generality of the model across different trees and seasons is supported by the lack of pattern with respect to plant when the residuals are plotted against both VPD and Ψsoil(the mean and mean absolute error of the residuals were almost identical across plants, see, e g., Figures 11A and 11B) and the lack of significant differences between estimated plant- and year-level effects (Figures 13A and 13B). In addition to the large percentage of variance explained, the validity of the model was demonstrated by the close correspondence between the observed and predicted data and the excellent calibration of the credible intervals (e.g., 96% of the data lie within the 95% credible interval, Figure 12), as well as the model convergence diagnostic R (all values were less than 1.003 where values must be below 1.01 for convergence). The five key parameters of the model introduced here (Figure 6) provide a holistic characterization of a species phenotype for both pressure regulation and water-use strategy. These parameters emerged from a mechanism-aligned model and should be readily and statistically comparable between species and genotypes.

[0130] Given the strong interaction effects of VPD andso;z in the model, the lack of Ψstempredictability in previous studies using the existing metrics, such as isohydricity and hydroscape, might arise from a failure to consider VPD and its interaction withso;7. The often-reported inconsistency in 'I'^m regulation as a function ofso,7 across different seasons of the same year is likely attributed to seasonal variation in VPD rather than alteration in stomatai behaviour given the strong influence of VPD on the magnitude of ΔΨ changes that were observed.

[0131] It is worth pointing out that, in addition to VPD and Ψsoil, ΔΨ may also be influenced by other factors such as changes in soil water viscosity due to fluctuation in soil temperature, especially at the seasonal time scale. In the present study, soil temperature was measured over one growing season in 2024 at 0.3 m depth, and it was found to vary by 3 degrees throughout growing season (between December 2023 and April 2024), hence may have a small impact. Additionally, changes in root: shoot ratio due to plant developmental or phenol ogical changes which might occur over long-term, such as throughout the growing season or across different years, may alsoinfluence relationship between ΔΨ to VPD. However, the lack of significant difference in θ₀ and λ₀ above the Ψsoilthreshold between and within individuals across different years (Figures 13A and 13B) suggests that root:shoot ratio is somewhat conserved and precisely regulated in this species, but the possibility of slight changes throughout a growing season remains. Nevertheless, the inclusion of these additional factors might help to reduce the remaining small portion of unexplained variance in the present model.Predicting whole plant diffuse conductance from pressure regulation

[0132] Stomata are the main regulator of VΨstem. Therefore, characterizing the regulation pattern of Vstemshould predict stomatai behaviour, especially if plant hydraulic conductance remains somewhat constant. Stomatai behaviour is often studied as a function of Ψsoilor VPD, independently. Here, whole plant stomatai responses to the combined effect of VPD and Ψsoilwere inferred from the modelled ΔΨ regulation (Figure 7). Under relatively hydrated conditions (Ψsoilclose to 0 MPa), whole plant stomatal diffusive conductance (gc) was predicted to decline exponentially and by more than 80% as VPD increases to 4 kPa, a behaviour reported in several species under wet conditions (Oren et al., 1999; Novick et al., 2016; Grossiord et al., 2018). As Ψsoildeclined due to soil drying, gcmaintained the same response relationship with VPD and showed no sensitivity to this decline until Ψsoilreached -0.83 MPa. Below this Ψsoilthreshold, stomata became highly sensitive to VPD and closed to a greater extent thereafter (Figure 7). This suggests that, in this species, VPD can be the main driver of carbon gain and water loss across the range of soil moisture conditions experienced during most of the growing season during average years such as 2023 where Ψsoilnever declined significantly below the threshold (Figure 1) (Ψsoilbetween 0 and -1 MPa). This prediction assumed constant Ks.pduring drying, however, applying the Ks-presponse to Ψsoilobserved in potted Callitris plants only increased the slope of the stomatal closure response below the Ψsoilthreshold.

[0133] The integrated gcresponse to VPD and Ψsoilis intriguing and has never been reported before due the fact that the response to these factors is often studied separately. Existing optimization models posit that stomata close to minimize the cost of reduction within or outside xylem hydraulic conductance caused by low water potential (Sperry & Love, 2015; Sperry et al., 2016; Wolf etal., 2016; Anderegg etal., 2018; Joshi etal., 2022). On the other hand, soil hydraulic models suggest that stomatai closure is optimal when it corresponds with declines in the rhizosphere hydraulic conductance (Carminati & Javaux, 2020). Both models however predict that stomata should remain open regardless of the change in Ψsoilor VPD conditions if the risk ofreduction in soil-plant hydraulic conductance is negligible. However, the theory behind these models does not perfectly align with the present data, which shows strong decline in gcat high VPD even in well hydrated soils (Ψsoilclose to 0 MPa) where no decline in Ks-pis expected in this species (Bourbia etal., 2021). This is further supported by evidence from a recent study reporting stomata to close at high VPD under ample soil moisture without any decline in Ks-Pfor the species studied here (Bourbia & Brodribb, 2024). On the other hand, the Ψsoiltrigger point for aggressive stomatal closure is rather close to the point of declining Krootfor the studied species Callitris rhomboidea (Bourbia et al., 2021) and the prediction for optimal soil water extraction (Carminati & Javaux, 2020). Therefore, the behaviour of gcin Callitris rhomboidea under specifically under drying soil does appear consistent with optimal regulation of transpiration to protect the xylem and to optimally extract water from the soil.Conclusion

[0134] Long term monitoring of Ψstemin four trees revealed a highly predictable regulatory behaviour of pressure and inferred stomatai conductance during highly variable growing seasons over 4 years. This characterization of a plant’s water use physiology opens the way for wider comparisons between genotypes and species, using a common monitoring and statistical protocol. The resultant potential for phenotypic screening in crop species is highly significant, while the ecological application for understanding and characterizing diversity among species in water use “strategy” will enable a deeper understanding of how plants compete and survive during seasonal variation in water availability.

[0135] In summary, the present invention provides a method for determining water-use regulatory behaviour in a plant (e.g., a plurality of plants, e.g., a plurality of plants comprising different plant species), based on measurements of climate parameters (such as temperature and humidity), and soil conditions (such as soil moisture levels). In some embodiments, the method also allows for the identification of a threshold for a plant where declining soil water content starts to limit plant activity. This can be used to provide actionable insights relating to the hydration and stress levels of plants. Advantageously, the method can be used to optimise irrigation schedule of plants in order to decrease water wastage and to affect the properties of the plant for a specific purpose (e.g., increasing yield, controlling growth / maturation (e.g., pit hardening, maturation index), reduction of post-harvest water loss, fruit quality, fruit size, skin thickness (e.g., tuber skin thickness), maintaining consistency in shape, and minimising defects). Moreover, the methods can be used for all plants, irrespective of species or genotype. Finally, in some embodiments, themethod can be performed non-invasively without the need to penetrate / damage the plants, which may be particularly useful in certain agricultural contexts.

[0136] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavor to which this specification relates.

[0137] Throughout this specification and the claims which follow, unless the context requires otherwise, the word ‘comprise’, and variations such as ‘comprises’ and ‘comprising’, will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.ReferencesAnderegg WRL, Wolf A, Arango-Velez A, Choat B, Chmura DJ, Jansen S, Kolb T, Li S, Meinzer FC, Pita P, et al. 2018. Woody plants optimise stomatai behaviour relative to hydraulic risk. Ecology Letters 21: 968-977.Bourbia I, Brodribb TJ.2024. Stomatai response to VPD is not triggered by changes in soil-leaf hydraulic conductance in Arabidopsis or Callitris. New Phytologist 242: 444-452.Bourbia I, Pritzkow C, Brodribb TJ. 2021. Herb and conifer roots show similar high sensitivity to water deficit. Plant Physiology 186: 1908-1918.Carminati A, Javaux M. 2020. Soil Rather Than Xylem Vulnerability Controls Stomatai Response to Drought. Trends in Plant Science 25: 868-880.Grossiord C, Buckley TN, Cernusak LA, Novick KA, Poulter B, Siegwolf RTW, Sperry JS, McDowell NG. 2020. Plant responses to rising vapor pressure deficit. New Phytologist 226: 1550-1566.Grossiord C, Sevanto S, Limousin J-M, Meir P, Mencuccini M, Pangle RE, Pockman WT, Salmon Y, Zweifel R, McDowell NG. 2018. Manipulative experiments demonstrate how long-term soil moisture changes alter controls of plant water use. Environmental and Experimental Botany 152: 19-27.Joshi J, Stocker BD, Hofhansl F, Zhou S, Dieckmann U, Prentice IC. 2022. Towards a unified theory of plant photosynthesis and hydraulics. Nature Plants 8: 1304-1316.Kangur O, Tullus A, Sellin A. 2020. Night-time transpiration, predawn hydraulic conductance and water potential disequilibrium in hybrid aspen coppice. Trees 34: 133-141.Kavanagh KL, Pangle R, Schotzko AD. 2007. Nocturnal transpiration causing disequilibrium between soil and stem predawn water potential in mixed conifer forests of Idaho. Tree Physiology 27: 621-629.Novick KA, Ficklin DL, Stoy PC, Williams CA, Bohrer G, Oishi AC, Papuga SA, Blanken PD, Noormets A, Sulman BN, et al. 2016. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nature Climate Change 6: 1023-1027.Oren R, Sperry JS, Katul GG, Pataki DE, Ewers BE, Phillips N, Schafer KVR. 1999. Survey and synthesis of intra- and interspecific variation in stomatai sensitivity to vapour pressure deficit. Plant, Cell & Environment 22: 1515-1526.Sellin A. 1999. Does pre-dawn water potential reflect conditions of equilibrium in plant and soil water status? Acta Oecologica 20: 51-59.Sperry JS, Love DM. 2015. What plant hydraulics can tell us about responses to climate-change droughts. New Phytologist 207: 14–27.Sperry JS, Wang Y, Wolfe BT, Mackay DS, Anderegg WRL, McDowell NG, Pockman WT. 2016. Pragmatic hydraulic theory predicts stomatal responses to climatic water deficits. New Phytologist 212: 577–589.Wolf A, Anderegg WRL, Pacala SW. 2016. Optimal stomatai behavior with competition for water and risk of hydraulic impairment. Proceedings of the National Academy of Sciences 113: E7222-E7230.

Claims

THE CLAIMS DEFINING THE INVENTION ARE AS FOLLOWS:

1. A method of quantifying the pressure regulatory behaviour in a plant, whereby the method includes the monitoring of an in-situ plant vascular water potential (Ψstem) under at least two different atmospheric dryness (VPD) conditions and at least two different soil water potential (Ψsoil) conditions.

2. A method for determining water-use regulatory behaviour in a plant including recording atmospheric evaporative demand (VPD), soil water potential (^o; / ), and plant vascular water potential ( Ψstem).

3. A method for determining water-use regulatory behaviour in a plant including recording atmospheric evaporative demand (VPD), soil water potential (T7™ / ), and plant vascular water potential ( Ψstem), wherein plant vascular water potential is measured using a non-invasive measurement device.

4. The method of any one of claims 1 to 3, further including a step of determining irrigation requirements of the plant.

5. The method of any one of claims 1 to 4, further including a step of determining a hydration level of the plant.

6. The method of any one of claims 1 to 5, further including a step of determining a daily stress exposure measurement for the plant.

7. The method of any one of claims 1 to 6, further including a step of optimising an irrigation schedule of the plant in order to optimise a yield of said plant.

8. The method of any one of claims 1 to 7, further including a step of recording seasonal exposures and environmental conditions.

9. The method of claim 3, or any one of claims 4 to 8 when appended to claim 3, wherein the non-invasive measurement device is attached to a leaf or a non-growing branchlet.

10. The method of claim 9, wherein the branchlet is less than approximately three millimetres in diameter.11 The method of claim 9 or claim 10, wherein the non-invasive measurement device comprises an optical dendrometry device.

12. The method of any one of claims 1 to 11, wherein the plant vascular water potential is recorded as daily maximum and minimum values.

13. The method of any one of claims 1 to 11, wherein measurements of the plant vascular water potential are recorded with an interval that is approximately of the order of minutes.

14. The method of any one of claims 1 to 13, wherein the recordings of atmospheric conditions and plant vascular water potential comprise single daily maximum values.

15. The method of any one of claims 1 to 14, further including a step of determining a vascular pressure phenotype (hydraulic phenotype).

16. The method of any one of claims 1 to 15, further including a step of inferring whole plant stomatal responses to the combined effect of atmospheric conditions and soil water potential.

17. The method of any one of claims 1 to 16, wherein the plant is not a woody species.

18. The method of any one of claims 1 to 17, wherein the plant is an agricultural crop plant species.

19. The method of any one of claims 1 to 17, wherein the plant is a fruit bearing plant species.

20. The method of any one of claims 1 to 17, wherein the plant is a tree.

21. The method of any one of claims 1 to 20, further including a step of comparing water-use regulatory behaviour between different plant species.

22. A system for characterising water-use regulatory behaviour in a plant, comprising:a device for measuring atmospheric evaporative demand (VPD);a sensor for measuring soil water potential (Ψsoil); andan instrument for measuring plant vascular water potential PΨstem),wherein data recorded by each of the device, the sensor, and the instrument are combined to determine a stress level of the plant.

23. The system of claim 22, wherein the data are used to determine a hydraulic phenotype of the plant.

24. The system of claim 22 or 23, further comprising a processor for determining irrigation requirements of the plant.

25. The system of any one of claims 22 to 24, further comprising an irrigator for irrigating the plant.

26. The system of any one of claims 22 to 25, wherein the instrument is a non-invasive instrument.

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